期刊
BIOINFORMATICS
卷 28, 期 4, 页码 573-580出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btr709
关键词
-
类别
资金
- Tiger University of the Estonian Information Technology Foundation
- EU [LSHG-CT-2005-518254]
- ESNATS [HEALTH-F5-2008-201619]
- European Regional Development Fund through the Estonian Centre of Excellence in Computer Science project
- Estonian Science Foundation [ETF7437]
Motivation: The continued progress in developing technological platforms, availability of many published experimental datasets, as well as different statistical methods to analyze those data have allowed approaching the same research question using various methods simultaneously. To get the best out of all these alternatives, we need to integrate their results in an unbiased manner. Prioritized gene lists are a common result presentation method in genomic data analysis applications. Thus, the rank aggregation methods can become a useful and general solution for the integration task. Results: Standard rank aggregation methods are often ill-suited for biological settings where the gene lists are inherently noisy. As a remedy, we propose a novel robust rank aggregation (RRA) method. Our method detects genes that are ranked consistently better than expected under null hypothesis of uncorrelated inputs and assigns a significance score for each gene. The underlying probabilistic model makes the algorithm parameter free and robust to outliers, noise and errors. Significance scores also provide a rigorous way to keep only the statistically relevant genes in the final list. These properties make our approach robust and compelling for many settings.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据